Sexual activity in young people is on the decline, but why? And what's more, should we be worried about what this means for society and the future of the human race? The comedy film was released in 1973 with a largely youthful cast and one too many double entendres. Half a century later, that title seems more apt than ever, at least among the younger members of society. Over the past few decades, sex appears to have been on the decline among teenagers and young adults - but it's not just happening in Britain . In the US in 2010, 12 per cent of 18 to 29-year-olds reported not having had sex in the past year, according to the General Social Survey, a long-running sociological survey.

Multi-agent reinforcement learning (MARL) holds substantial promise for intelligent decision-making in complex environments. However, it suffers from a coordination and scalability bottleneck as the number of agents increases. To address these issues, we propose the LLM-empowered expert demonstrations framework for multi-agent reinforcement learning (LEED). LEED consists of two components: a demonstration generation (DG) module and a policy optimization (PO) module. Specifically, the DG module leverages large language models to generate instructions for interacting with the environment, thereby producing high-quality demonstrations. The PO module adopts a decentralized training paradigm, where each agent utilizes the generated demonstrations to construct an expert policy loss, which is then integrated with its own policy loss. This enables each agent to effectively personalize and optimize its local policy based on both expert knowledge and individual experience. Experimental results show that LEED achieves superior sample efficiency, time efficiency, and robust scalability compared to state-of-the-art baselines.

Three far-right extremists who amassed hundreds of weapons and planned to carry out attacks on targets including a mosque have been convicted of terrorism offences. Brogan Stewart, 25, from West Yorkshire, Christopher Ringrose, 34, from Staffordshire, and Marco Pitzettu, 25, from Derbyshire, were part of an online group who "idolised the Nazi regime". Sheffield Crown Court was told how Stewart had detailed torturing a Muslim leader using an "information extraction kit". All three were found guilty of terrorism offences at the same court on Wednesday and are due to be sentenced on 17 July.Counter Terrorism Policing North EastThe trio had amassed a cache of weapons as part of their planning During the nine-week trial, the court heard more than 200 weapons including machetes, hunting knives, swords and crossbows were found at their homes. Ringrose had also begun to build a 3D-printed semi-automatic firearm, which counter-terror police said would have been a "lethal weapon".

Fixed Term Contract - 6 Months BigChange wishes to complete a contract assurance review of its 6000 customer contracts. This will involve the reading of contract documentation stored in multiple systems, defining the terms of these agreements and confirming the accuracy of the current contract system set up for each. Legacy variations where found will need to be notified to our in house Sales Operations teams for remedial rectification and then following each contract will need to be setup in a prescribed manner within our systems for the future term. BigChange is officially an Outstanding Company to Work For, according to Best Companies 2021. On the independent Glassdoor website, we have a 4.6 out of 5 rating.

Online meta-learning has recently emerged as a marriage between batch meta-learning and online learning, for achieving the capability of quick adaptation on new tasks in a lifelong manner. However, most existing approaches focus on the restrictive setting where the distribution of the online tasks remains fixed with known task boundaries. In this work, we relax these assumptions and propose a novel algorithm for task-agnostic online meta-learning in non-stationary environments. More specifically, we first propose two simple but effective detection mechanisms of task switches and distribution shift based on empirical observations, which serve as a key building block for more elegant online model updates in our algorithm: the task switch detection mechanism allows reusing of the best model available for the current task at hand, and the distribution shift detection mechanism differentiates the meta model update in order to preserve the knowledge for in-distribution tasks and quickly learn the new knowledge for out-of-distribution tasks. In particular, our online meta model updates are based only on the current data, which eliminates the need of storing previous data as required in most existing methods. We further show that a sublinear task-averaged regret can be achieved for our algorithm under mild conditions. Empirical studies on three different benchmarks clearly demonstrate the significant advantage of our algorithm over related baseline approaches.

Researchers from the University of Leeds have developed the first mini-robot, called Joey, that can find its own way independently through networks of narrow pipes underground, to inspect any damage or leaks. Joeys are cheap to produce, smart, small, and light, and can move through pipes inclined at a slope or over slippery or muddy sediment at the bottom of the pipes. Future versions of Joey will operate in swarms, with their mobile base on a larger'mother' robot Kanga, which will be equipped with arms and tools for repairs to the pipes. Beneath our streets lies a maze of pipes, conduits for water, sewage, and gas. Regular inspection of these pipes for leaks, or repair, normally requires these to be dug up.

An updated version of artificial intelligence chatbot ChatGPT was launched by OpenAI on November 30. Its ability to write in an intelligent and human-like manner left users impressed -- and also a little bit frightened. People have used ChatGPT to write entire blocks of code, television scripts, and even complete academic essays -- sparking fears that students might use the bot to cheat their way to an easy A. "We're witnessing the death of the college essay in realtime," said one user on Twitter. But some college professors aren't that concerned. "I'm not a huge fan of the gloom and doom," said Professor Stuart Selber, who teaches English at Pennsylvania State University.

Move to Leeds and benefit from the jobs boom, says Melissa Berthelot, boss of medical appliance maker WarnerPatch, who relocated her business from London two years ago to benefit from a burgeoning deep tech industry in the West Yorkshire city. With skilled data science and software engineers in short supply across the south-east – and most other parts of the country – Leeds has proved a happy hunting ground for Berthelot, an engineer turned chief executive who used the first lockdown to make the jump north. Deep tech refers to sectors including artificial intelligence, robotics and bio-technologies. Its Blade Runner-like image may seem worlds away from the Emmerdale village tour on offer just west of town, but Leeds is managing to straddle old and new as it jumps up the UK rankings for job creation and productivity. The city has gained a reputation for developing the skilled staff and financial muscle needed to fund startups and innovation, especially in healthcare, but also in the city's more traditional areas of expertise – financial and legal services, manufacturing and retail.

Artificial intelligence (AI) can analyse large amounts of data, such as images or trial results, and can identify patterns often undetectable by humans, making it highly valuable in speeding up disease detection, diagnosis and treatment. However, using the technology in medical settings is controversial because of the risk of accidental data release and many systems are owned and controlled by private companies, giving them access to confidential patient data -- and the responsibility for protecting it. The researchers set out to discover whether a form of AI, called swarm learning, could be used to help computers predict cancer in medical images of patient tissue samples, without releasing the data from hospitals. Swarm learning trains AI algorithms to detect patterns in data in a local hospital or university, such as genetic changes within images of human tissue. The swarm learning system then sends this newly trained algorithm -- but importantly no local data or patient information -- to a central computer.

Artificial intelligence (AI) can analyse large amounts of data, such as images or trial results, and can identify patterns often undetectable by humans, making it highly valuable in speeding up disease detection, diagnosis and treatment. But using the technology in medical settings can be controversial because of the risk of accidental data release. Many systems are owned and controlled by private companies, giving them access to confidential patient data – and the responsibility for protecting it. A team of researchers has set out to discover whether a form of AI called swarm learning could be used to help computers predict cancer in medical images of patient tissue samples, without releasing the data from hospitals. Their research, titled'Swarm learning for decentralized artificial intelligence in cancer histopathology', was published on April 25 in Nature Magazine.